Fingerprint “Master Keys” Generated with Neural Network Could Break Your Phone’s Password

Running the work with biometrics is always a balancing act. By using passwords, authentication is simple — either it matches, or it fails if not correct. But when that "password" is part of a part someone’s body, whether an iris match, face scan, or just a usual old fingerprint, mobile systems have to anticipate and account for a hint of wiggle room.

In spite of everything, one would never want that face scan failing because he got a pimple on his face, or his fingerprint got rejected just because of touching the sensor at a slightly different angle each time. However, now a new attack is taking advantage of the flexibility programmed into user’s mobile phone systems by creating fake "universal" fingerprints.

These fake (synthetic) fingerprints, which are generally called “DeepMasterPrints” mostly by the researchers, were created by feeding a neural network image of the original fingerprints until it could generate its own.

Most of these prints were then analyzed using the same kind of certification algorithms employed by the scanners on users’ phones and customized again and again in slight ways until they passed — even though they didn't really match.

By doing this several times with a large data set, the team could become able to come up with fingerprint images that have a sufficient amount of elements in common with the prints of an average person. These prints were the mains, over which the researchers claimed that scanners could readily be tricked into giving a false positive. However, this isn't a matter of matching to just one individual, either — the “DeepMasterPrints” are intended to be working equally as well with any user.

How well, exactly? It relies on how challenging or demanding the scanner you're attempting to fool is. All fingerprint scanners have to believe some rate of false positives — circumstances where an unauthorized or illegal print gets interpreted by mistake as authorized or legal. A very broadminded scanner might let through 1.0% of false positives when the real fingerprints are in use, but the “DeepMasterPrints” has an ability to fool that kind of scanner an awful 77% of the time.

The “Stricter Scanners” with not more than 0.1% false positives are still tricked by the “DeepMasterPrints” more than 22% of the time; And even ones denying the entire, but the 0.01% of illegal prints, is always going to fall for “DeepMasterPrints.”

Nothing of this has been quite enough to make us full-on reject the plan of biometrics and return to just start using PINs and passwords, but now it's undoubtedly a shocking look into just how much level of security we're giving up for the sake of expediency and convenience. Thus, all we can do is hoping that future devices will be built with attacks like this in mind (programs), and offer some stronger false-positive rejection.

Mathew Anderson is a Microsoft Office expert and has been working in the technical industry since 2002. As a technical expert, Mathew has written technical blogs, manuals, white papers, and reviews for many websites such as


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